Digital photographers, scientific researchers, and other users of digital images often generate and store large repositories of digital image files that they wish to search for those images meeting certain criteria. For example, a reviewer writing a comparison test of different digital camera models may take dozens or hundreds of test shots with each of the digital camera models, and need to identify and organize those occurring under certain lighting conditions (e.g. bright sunlight, moonlight, etc.) to evaluate the comparative strengths of the cameras in such conditions.
Most existing systems allow users to search a repository by pre-defined classes. For example, digital images in a repository may be classified by scene types. Scene types may include, without limitation, landscape, portrait, close-ups, night scene, fast motion scene, back-lit scene, etc. Alternatively or in combination, classes can be determined by considering common attributes (e.g., operational conditions) associated with different sets of digital images then assigning names to identify the sets of digital images. In response to a search query identifying a class, existing systems generally retrieve and display a set of digital images that falls within that class.
The searchability provided by the existing systems is limited by the selection of pre-defined classes. That is, a user generally cannot search for images that have not been previously classified. In addition, responses provided by the existing system do not provide the mechanism or adequate information to allow a user to refine its searches.
Thus, a market exists for an improved digital image search and retrieval system that enables a user to perform more flexible searches.